Search Results (1,620)

Locusta migratoria manilensis (Meyen) is a highly destructive insect pest worldwide. However, excessive reliance on insecticides has resulted in significant environmental pollution. Biocontrol complexes combine two or more BCAs to address the limitations of individual agents. However, biocontrol complex for locust control has been rarely reported. Here, we propose BioControl 3.0, which integrates Metarhizium anisopliae var. acridum (Driver and Milner) and Carabus smaragdinus (Fischer von Waldheim) for locust control. We evaluated this system through a series of laboratory bioassays and semi-field cage experiments, comparing single-agent applications, sequential combinations (BioControl 2.0), and predator-mediated delivery (BioControl 3.0), and quantified locust mortality and interaction effects between predation and infection We found that M. anisopliae caused >85% mortality of locust nymphs at 1 × 10⁸ conidia/mL (LT₅₀ ≈ 6 days) while exhibiting negligible virulence toward C. smaragdinus. BioControl 2.0 (sequential application) increased mortality compared to single agents. However, this approach revealed a significant negative interaction between predation and infection, which limited the total control efficacy. BioControl 3.0 (predator-vectored fungus) achieved the highest corrected mortality, with predation and infection acting independently and additively (no detectable antagonistic interaction). By leveraging a predatory vector, BioControl 3.0 decouples negative interaction and harnesses dual biotic pressures, offering a cost-effective, environmentally benign alternative to conventional locust control. Our findings provide a blueprint for designing integrated predator-pathogen complexes and optimizing deployment strategies for sustainable management of locust outbreaks. Full article

In this study, a multienzyme isothermal and lateral flow dipstick combination assay for PPRV detection was established, the designed primers and probes targeting the N gene were screened and optimized, and analytical sensitivity, specificity, and repeatability of developed method were systematically evaluated. The experimental results demonstrated that this method is easy to operate, can complete detection within 30 min at 42 °C, and is capable of detecting all lineages of peste des petits ruminants virus (PPRV) without cross-reactivity with other viruses. The limit of detection could reach 10 copies/μL. Repeatability validation showed that the coefficients of variation (CV) for both intra-assay and inter-assay experiments were below 3.0%. The positive detection rate for clinical samples could reach 100%. The test results are visually interpretable via fluorescence and lateral flow strips. In conclusion, this method exhibits high analytical sensitivity, specificity, and excellent repeatability, enabling rapid diagnosis of peste des petits ruminants (PPR). Full article

The growing global population and increasing food demand highlight the need for sustainable agricultural practices that balance productivity with environmental protection. Traditional blanket pesticide spraying leads to overuse of chemicals, environmental pollution, and biodiversity loss. This study aims to develop an innovative approach to precision pest management using mobile computing, computer vision, and deep learning techniques. A mobile measurement platform equipped with cameras and an onboard computer was designed to collect real-time field data and detect pest infestations. The system uses an advanced object detection algorithm based on the YOLOv4 architecture, trained on a custom dataset of rapeseed pest images. Modifications were made to enhance detection accuracy, especially for small objects. Field tests demonstrated the system’s ability to identify and count pests, such as the pollen beetle (Brassicogethes aeneus), in rapeseed crops. The collected data, combined with GPS information, generated pest density maps, which can guide site-specific pesticide applications. The results show that the proposed method achieved a mean average precision (mAP) of 83.7% on the test dataset. Field measurements conducted during the traversal of rapeseed fields enabled the creation of density maps illustrating the distribution of pollen beetles. Based on these maps, the potential for pesticide savings was demonstrated, and the migration dynamics of pollen beetle were discussed. Full article

ISO 22002-100:2025 introduces stringent and more technically explicit prerequisite programme (PRP) requirements for allergen management, food fraud mitigation, and the control of chemical and packaging-related contaminants across the food, feed, and packaging supply chain. This review examines how advanced chromatographic methods provide the analytical basis required to meet these requirements and to support alignment with GFSI-recognized certification schemes. Recent applications of liquid and gas chromatography coupled with mass spectrometry for allergen quantification, authenticity assessment, and the determination of packaging migrants, auxiliary chemical residues, lubricants, and indoor pest-control pesticides are presented to demonstrate their relevance as verification tools. Across these PRP-related controls, chromatographic methods enable trace-level detection, structural specificity, and reproducible measurement performance, thereby shifting PRP compliance from a documentation-based activity to a process verified through measurable analytical evidence. The review highlights significant progress in method development and simultaneous multi-target analytical approaches while also identifying remaining challenges related to matrix-appropriate validation, harmonization, and analytical coverage for chemical contamination, which is now formally defined as a measurable PRP requirement under ISO 22002-100:2025. Overall, the findings demonstrate that chromatographic analysis has become essential to demonstrating PRP effectiveness under ISO 22002-100:2025, supporting the broader shift toward evidence-based, scientifically robust food safety assurance. Full article

The rapid evolution of insecticide resistance in Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) threatens effective pest management in key crops. This study characterized B. tabaci populations from cotton and tomato fields in Kashgar (September–October 2024) using mtCOI-RFLP for cryptic species identification, leaf-dip bioassays with 13 insecticides, and PCR detection of tomato yellow leaf curl virus (TYLCV). All analyzed individuals belonged to the Mediterranean (MED) cryptic species. Extreme resistance was observed to imidacloprid (RR = 320.65) and pyridaben (RR = 331.29), while nitenpyram (RR = 1.77) and the emamectin benzoate–chlorantraniliprole mixture (RR = 2.13) remained effective. TYLCV was detected in 97.5% of adults from tomato greenhouses. These findings provide a concise assessment of resistance status, species identification, and virus prevalence in B. tabaci, informing sustainable management strategies in cotton and tomato production. Full article

The temperate Himalayan states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Ladakh, Sikkim, and Arunachal Pradesh in India face unique agro-ecological challenges across agriculture and allied sectors, including pest and disease pressures, inefficient resource use, post-harvest losses, and fragmented supply chains. This review systematically examines 21 critical problem areas, with three key challenges identified per sector across agriculture, agricultural engineering, fisheries, forestry, horticulture, sericulture, and animal husbandry. Artificial Intelligence (AI) and Machine Learning (ML) interventions, including computer vision, predictive modeling, Internet of Things (IoT)-based monitoring, robotics, and blockchain-enabled traceability, are evaluated for their regional applicability, pilot-level outcomes, and operational limitations under temperate Himalayan conditions. The analysis highlights that AI-enabled solutions demonstrate strong potential for early pest and disease detection, improved resource-use efficiency, ecosystem monitoring, and market integration. However, large-scale adoption remains constrained by limited digital infrastructure, data scarcity, high capital costs, low digital literacy, and fragmented institutional frameworks. The novelty of this review lies in its cross-sectoral synthesis of AI/ML applications tailored to the Himalayan context, combined with a sector-wise revenue-loss assessment to quantify economic impacts and guide prioritization. Based on the identified gaps, the review proposes feasible, context-aware strategies, including lightweight edge-AI models, localized data platforms, capacity-building initiatives, and policy-aligned implementation pathways. Collectively, these recommendations aim to enhance sustainability, resilience, and livelihood security across agriculture and allied sectors in the temperate Himalayan region. Full article

Traditional plant scouting is often a costly and labor-intensive task that requires experienced specialists to diagnose and manage plant stresses. Artificial intelligence (AI), particularly deep learning and computer vision, offers the potential to transform scouting by enabling rapid, non-intrusive detection and classification of early stress symptoms from plant images. However, deep learning models are often opaque, relying on millions of parameters to extract complex nonlinear features that are not interpretable by growers. Recently, eXplainable AI (XAI) techniques have been used to identify key spatial regions that contribute to model predictions. This project explored the potential of convolutional neural networks (CNNs) for classifying the severity of chilli thrips damage in strawberry plants in Florida and employed XAI techniques to interpret model decisions and identify symptom-relevant canopy features. Four CNN architectures, YOLOv11, EfficientNetV2, Xception, and MobileNetV3, were trained and evaluated using 2353 square RGB canopy images of different sizes (256, 480, 640 and 1024 pixels) to classify symptoms as healthy, moderate, or severe. Trade-offs between image size, model parameter count, inference speed, and accuracy were examined in determining the best-performing model. The models achieved accuracies ranging from 77% to 85% with inference times of 5.7 to 262.3 ms, demonstrating strong potential for real-time pest severity estimation. Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization revealed that model attention focused on biologically relevant regions such as fruits, stems, leaf edges, leaf surfaces, and dying leaves, areas commonly affected by chilli thrips. Subsequent analysis showed that model attention spread from localized regions in healthy plants to wide diffuse regions in severe plants. This alignment between model attention and expert scouting logic suggests that CNNs internalize symptom-specific visual cues and can reliably classify pest-induced plant stress. Full article

Ensuring food security requires innovative, sustainable pest management solutions. The Solar Insecticidal Lamp Internet of Things (SIL-IoT) represents such an advancement, yet its reliability in harsh, variable outdoor environments is compromised by frequent component and sensor faults, threatening effective pest control and data integrity. This paper presents a comprehensive survey on fault detection (FD) for SIL-IoT systems, systematically analyzing their unique challenges, including electromagnetic interference, resource constraints, data scarcity, and network instability. To address these challenges, we investigate countermeasures, including blind source separation for signal decomposition under interference, lightweight model techniques for edge deployment, and transfer/self-supervised learning for low-cost fault modeling across diverse agricultural scenarios. A dedicated case study, utilizing sensor fault data of SIL-IoT, demonstrates the efficacy of these approaches: an empirical mode decomposition-enhanced model achieved 97.89% accuracy, while a depthwise separable-based convolutional neural network variant reduced computational cost by 88.7% with comparable performance. This survey not only synthesizes the state of the art but also provides a structured framework and actionable insights for developing robust, efficient, and scalable FD solutions, thereby enhancing the operational reliability and sustainability of SIL-IoT systems. Full article

Thrips are cosmopolitan agricultural pests and important vectors of plant viruses, and the increasing coexistence of multiple morphologically similar species has intensified the demand for species-specific molecular identification. However, traditional morphological identification and PCR assays using universal primers are often inadequate for mixed-species samples and field-adaptable application. In this study, we developed a species-specific molecular identification framework targeting a polymorphism-rich region of the mitochondrial cytochrome c oxidase subunit I (COI) gene, which is more time-efficient than sequencing-based COI DNA barcoding, for four economically important thrips species in southern China, including the globally invasive Frankliniella occidentalis. By aligning COI sequences, polymorphism-rich regions were identified and used to design four species-specific primer pairs, each containing a diagnostic 3′-terminal nucleotide. These primers were combined with a PBS-based DNA extraction workflow optimized for single-insect samples that minimizes dependence on column-based purification. The assay achieved a practical detection limit of 1 ng per reaction, demonstrated species-specific amplification, and maintained reproducible amplification at DNA inputs of ≥1 ng per reaction. Notably, PCR inhibition caused by crude extracts was effectively alleviated by fivefold dilution. Although the chemical identities of the inhibitors remain unknown, interspecific variation in inhibition strength was observed, with T. hawaiiensis exhibiting the strongest suppression, possibly due to differences in lysate composition. This integrated framework balances target specificity, operational simplicity, and dilution-mitigated inhibition, providing a field-adaptable tool for thrips species identification and invasive species monitoring. Moreover, it provides a species-specific molecular foundation for downstream integration with visual nucleic acid detection platforms, such as the CRISPR/Cas12a system, thereby facilitating the future development of portable molecular identification workflows for small agricultural pests. Full article

Cnidium monnieri (L.) Cusson is a species of Umbelliferae plants, and it is one of China’s traditional medicinal herbs, widely distributed in China owing to its strong adaptability in fields. In this article, the research progress on the taxonomy, distribution, cultivation techniques, active components, analysis methods, antibacterial and insecticidal properties, and ecological applications of C. monnieri was reviewed. The main active components in C. monnieri are coumarins (mainly osthole) and volatile compounds, exhibiting multiple pharmacological effects, e.g., anti-inflammatory, antibacterial, antioxidant, anti-tumor, and immune-regulating effects. Some modern analytical techniques (e.g., HPLC, GC-MS, and UPLC-QTOF-MS) have enabled more precise detection and quality control of these chemical components in C. monnieri. The specific active constituents in C. monnieri (e.g., coumarins and volatile components) exhibit significant inhibitory effects against various pathogenic fungi and insect pests. Simultaneously, the resources provided during its flowering stage (e.g., pollen and nectar) and the specific volatiles released can repel herbivorous insect pests while attracting natural enemies, such as ladybugs, lacewings, and hoverflies, thereby enhancing ecological control of insect pests in farmland through a “push–pull” strategy. Additionally, C. monnieri has the ability to accumulate heavy metals, e.g., Zn and Cu, indicating its potential value for ecological restoration in agroecosystems. Overall, C. monnieri has medicinal, ecological, and economic value. Future research should focus on regulating active-component synthesis, improving our understanding of ecological mechanisms, and developing standardized cultivation systems to enhance the applications of C. monnieri in modernized traditional Chinese medicine and green agriculture production. Full article

Common methods for detecting Drosophila suzukii (spotted-wing drosophila, SWD) in fruit, such as microscopy, physical extraction, and incubation, are time-consuming and may underrepresent egg and first instar larvae counts, the smallest life stages of SWD. To address these limitations, we evaluated a quantitative real-time PCR (qPCR) protocol to detect and quantify SWD eggs using a linear model of the log-transformed ratio of eggs to sample volume (µL) in Tris buffer and fruit tissue. Compared to traditional approaches, this method reduces identification time from several weeks to approximately five hours. We observed a negative linear correlation between qPCR cycle threshold and egg concentration in both standard and fruit tissue samples, with similar model fits (R² = 0.7215 for field fruit tissue; R² = 0.874 for standard samples). This DNA-based protocol improves infestation detection speed and accuracy by enabling rapid, species-specific identification of D. suzukii in fruit tissue, addressing limitations of morphological identification of eggs and larvae. Further refinement for fruit tissue could enhance real-world applicability. Rapid detection may enable timely assessment of varietal resistance to SWD and support safer control strategies targeting early life stages, helping to prevent pest development and fruit degradation. Full article

Meteorological data from automatic weather stations (AWS) in tea plantations is critical for agricultural management, but is often compromised by sensor errors and physical implausibilities that traditional quality control (QC) methods fail to detect. This study proposes a novel, meteorologically informed QC framework that mines association rules from long-term ERA5 reanalysis data (2012–2023) using the Apriori algorithm to establish a knowledge base of normal multivariate atmospheric patterns. A comprehensive feature engineering process generated temporal, physical, and statistical features, which were discretized using meteorological thresholds. The mined rules were filtered, prioritized, and integrated with hard physical constraints. The system employs a fuzzy logic mechanism for violation assessment and a weighted anomaly scoring system for classification. When validated on a synthetic dataset with injected anomalies, the method significantly outperformed traditional QC techniques, achieving an F1-score of 0.878 and demonstrating a superior ability to identify complex physical inconsistencies. Application to an independent historical dataset from a Zhenjiang tea plantation (2008–2016) successfully identified 14.6% anomalous records, confirming the temporal transferability and robustness of the approach. This framework provides an accurate, interpretable, and scalable solution for enhancing the quality of meteorological data, with direct implications for improving the reliability of frost prediction and pest management in precision agriculture. Full article

Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic in Hungary, endangering crop productivity and public health through its fast proliferation and allergenic pollen. This review examines the current challenges and impacts of A. artemisiifolia while exploring sustainable approaches to its management through precision agriculture. Recent advancements in unmanned aerial vehicles (UAVs) equipped with advanced imaging systems, remote sensing, and artificial intelligence, particularly deep learning models such as convolutional neural networks (CNNs) and Support Vector Machines (SVMs), enable accurate detection, mapping, and classification of weed infestations. These technologies facilitate site-specific weed management (SSWM) by optimizing herbicide application, reducing chemical inputs, and minimizing environmental impacts. The results of recent studies demonstrate the high potential of UAV-based monitoring for real-time, data-driven weed management. The review concludes that integrating UAV and AI technologies into weed management offers a sustainable, cost-effective, mitigate the socioeconomic impacts and environmentally responsible solution, emphasizing the need for collaboration between agricultural researchers and technology developers to enhance precision agriculture practices in Hungary. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

Pesticides have been widely applied in agricultural practices over the past decades to protect crops from pests and other harmful organisms. However, their extensive use results in the contamination of soil, water, and agricultural products, posing significant risks to human and environmental health. Exposure to pesticides can lead to skin irritation, respiratory disorders, and various chronic health problems. Moreover, pesticides frequently enter surface water bodies such as rivers and lakes through agricultural runoff and leaching processes. Therefore, developing effective analytical methods for the rapid and sensitive detection of pesticides in food and water is of great importance. Electrochemical sensing techniques have shown remarkable progress in pesticide analysis due to their high sensitivity, simplicity, and potential for on-site monitoring. Two-dimensional (2D) carbon nanomaterials have emerged as efficient electrocatalysts for the precise and selective detection of pesticides, owing to their large surface area, excellent electrical conductivity, and unique structural features. In this review, we summarize recent advancements in the electrochemical detection of pesticides using 2D carbon-based materials. Comprehensive information on electrode fabrication, sensing mechanisms, analytical performance—including sensing range and limit of detection—and the versatility of 2D carbon composites for pesticide detection is provided. Challenges and future perspectives in developing highly sensitive and selective electrochemical sensing platforms are also discussed, highlighting their potential for simultaneous pesticide monitoring in food and environmental samples. Carbon-based electrochemical sensors have been the subject of many investigations, but their practical application in actual environmental and food samples is still restricted because of matrix effects, operational instability, and repeatability issues. In order to close the gap between laboratory research and real-world applications, this review critically examines sensor performance in real-sample conditions and offers innovative approaches for in situ pesticide monitoring. Full article